Estimating Riverwide Abundance of Juvenile Fish Populations: How Much Sampling is Enough?
Bibliographic record
Abstract
Abstract Estimating riverwide abundance of juvenile fish populations is challenging because detection probability is typically low and juveniles can be patchily distributed over large areas. We used a hierarchical Bayesian model to estimate the abundance of juvenile steelhead Oncorhynchus mykiss in two rivers in British Columbia over 3 years based on a multigear, two-phase sampling design. These estimates were used to drive a simulation model to evaluate how the precision of abundance estimates varied with the number of single-pass index and mark–recapture sites that were sampled, the proportion of shoreline sampled, and the mean and variation of detection probability and fish density across sites. The extent of variation in fish densities across index sites was the most important factor influencing the precision of river-wide abundance estimates, and increasing the number of index sites was the best approach to reduce variability in abundance estimates. River size, which controls the proportion of habitat sampled for a given level of sampling effort, had a moderate effect on precision, but only when the extent of site-to-site variation in fish density was high. Factors affecting detection probability, such as the number of mark–recapture sites, the mean detection probability, or the extent of variation in detection probability across sites, had much less influence on precision of abundance estimates unless the proportion of river sampled was high. Hierarchical Bayesian models are no substitute for collecting informative data, but they improve our understanding of variance structure, which is critical for providing realistic estimates of uncertainty and designing informative and efficient sampling programs. Received July 16, 2015; accepted October 7, 2015 Published online March 8, 2016
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".